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Issue title: Some highlights on fuzzy systems and data mining
Guest editors: Shilei Sun, Silviu Ionita, Eva Volná, Andrey Gavrilov and Feng Liu
Article type: Research Article
Authors: Wang, Xiaoxiaa; * | Yang, Fengbaoa | Wei, Hongb | Ji, Linnaa
Affiliations: [a] School of Information and Communication Engineering, North University of China, Taiyuan, Shanxi, China | [b] Computational Vision Group, School of Systems Engineering, University of Reading, Reading, UK
Correspondence: [*] Corresponding author. Xiaoxia Wang, School of Information and Communication Engineering, North University of China, No. 3 Xueyuan Lu, 030051, Taiyuan, Shanxi, China. Tel.: +86 13753167491; Fax: +86 351 3923623; E-mail: wangxiaoxia@nuc.edu.cn.
Abstract: Because a single model cannot determine a complicated mapping relationship, monitoring index and risk rank is important; a reservoir water level monitoring index in the tailing dam is posed by a multiple set value map. First, the information from a reservoir water level and risk rank of the tailings dam is collected, characterizing and constructing corresponding domains with multiple possible values. Second, by extending a point mapping to a fuzzy set mapping, various set-valued mapping relationships between the reservoir water level and the risk rank are established, which obtains joint-falling shadows. Third, by evaluating the risk rank of the tailings dam, a risk assessment model between the reservoir water level and the risk rank is proposed, which fuses the multiple set-valued mapping and configuration and combines the results. Finally, the proposed method is verified by an example. The results show that not only is the proposed method a new method for establishing the complex mapping relationship between the reservoir water level and the risk rank of the tailings dam, but also for evaluating results that are consistent with the actual situation of the dam.
Keywords: Uncertainty system, risk assessment, set-valued mapping relationship
DOI: 10.3233/JIFS-169201
Journal: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 6, pp. 3155-3162, 2016
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